title: Efficient parameter estimation for models of healthcare-associated pathogen transmission in discrete and continuous time.
abstract: We describe two novel Markov chain Monte Carlo approaches to computing estimates of parameters concerned with healthcare-associated infections. The first approach frames the discrete time, patient level, hospital transmission model as a Bayesian network, and exploits this framework to improve greatly on the computational efficiency of estimation compared with existing programs. The second approach is in continuous time and shares the same computational advantages. Both methods have been implemented in programs that are available from the authors. We use these programs to show that time discretization can lead to statistical bias in the underestimation of the rate of transmission of pathogens. We show that the continuous implementation has similar running time to the discrete implementation, has better Markov chain mixing properties, and eliminates the potential statistical bias. We, therefore, recommend its use when continuous-time data are available.
authors.affiliation: Division of Genetic Epidemiology, 391 Chipeta Way Suite D, Salt Lake City, UT 84108, USA alun@genepi.med.utah.edu.
authors.forename: Alun
authors.initials: A
authors.lastname: Thomas
authors.affiliation: Division of Epidemiology, University of Utah, Salt Lake City, UT, USA and VA Salt Lake City Health Care System, Salt Lake City, UT, USA.
authors.forename: Andrew
authors.initials: A
authors.lastname: Redd
authors.affiliation: Division of Epidemiology, University of Utah, Salt Lake City, UT, USA and VA Salt Lake City Health Care System, Salt Lake City, UT, USA.
authors.forename: Karim
authors.initials: K
authors.lastname: Khader
authors.affiliation: Division of Epidemiology, University of Utah, Salt Lake City, UT, USA and VA Salt Lake City Health Care System, Salt Lake City, UT, USA.
authors.forename: Molly
authors.initials: M
authors.lastname: Leecaster
authors.affiliation: Division of Epidemiology, University of Utah, Salt Lake City, UT, USA and VA Salt Lake City Health Care System, Salt Lake City, UT, USA.
authors.forename: Tom
authors.initials: T
authors.lastname: Greene
authors.affiliation: Division of Epidemiology, University of Utah, Salt Lake City, UT, USA and VA Salt Lake City Health Care System, Salt Lake City, UT, USA.
authors.forename: Matthew
authors.initials: M
authors.lastname: Samore
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D003428:Cross Infection / Q000635:transmission*; D006787:Hospitals, Veterans; D006801:Humans; D007362:Intensive Care Units; D008390:Markov Chains; D055641:Mathematical Concepts; D055624:Methicillin-Resistant Staphylococcus aureus; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D012984:Software; D013203:Staphylococcal Infections / Q000635:transmission
keywords: Bayesian networks; Markov chain Monte Carlo integration; bacterial colonization; infectious disease transmission; nosocomial infection; statistical bias; susceptible-infected models